We are seeking an AI Engineer with 5+ years of experience in developing Python-based, cloud-native applications that leverage natural language processing, GenAI/LLMs, and agentic workflows to automate manual analysis, understand customer intent, and guide users to the appropriate cryptographic services.

The ideal candidate has hands-on experience with modern AI/ML architectures, and containerized microservices, and enjoys working on end-to-end systems spanning data ingestion, AI orchestration, and secure cloud infrastructure.

Key Responsibilities

Design and develop Python-based applications and services to automate internal customer engagement and onboarding workflows.
Implement GenAI solutions using LLMs, embeddings, vector databases, and retrieval-augmented generation (RAG) architectures.
Build NLP-enabled agents that can understand natural-language requests and assist users in selecting the appropriate cryptographic services.
Automate manual analysis and triage processes using AI/ML and rule-based logic.
Build and manage knowledge bases, embedding pipelines, and contextual retrieval systems for accurate, reliable responses.
Design agentic workflows and orchestration logic to support decision-making and process automation.
Collaborate with product, data, and engineering teams to translate business and compliance requirements into technical solutions.
Monitor, troubleshoot, and optimize production workloads for performance, reliability, and cost efficiency.

Required Skills & Experience

5+ years of professional experience in software engineering, cloud engineering, or AI/ML development.
Strong proficiency in Python (experience with FastAPI, Flask, or similar frameworks preferred).
Experience with Docker, GitHub, and CI/CD automation.
Working knowledge of LLMs, prompt engineering, embeddings, and GenAI concepts.
Experience building or contributing to RAG pipelines or search/knowledge-based AI systems.
Familiarity with vector databases (e.g., Pinecone, FAISS, Redis, OpenSearch, or similar).
Exposure to agentic frameworks (e.g., LangChain, LlamaIndex, or custom agent implementations).
Understanding of API development, distributed systems, and cloud-native design patterns.
Exposure to security-focused or regulated environments (financial services, cryptography, compliance).
Familiarity with infrastructure-as-code tools such as CloudFormation or CDK.
Experience working on internal enterprise platforms rather than consumer applications.

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